Manufacturers are often tasked with creating objects with properties satisfying certain specifications. For example, a specification may be made that an object will not break easily when pulled, thus requiring tensile strength of at least 100 megapascals (MPa). Exhaustively testing large amounts of, or various sets of, candidate materials to determine whether they satisfy specified manufacturing parameters requires immense expenditure of time, manpower, and funds, and is commercially infeasible.
Existing systems access data from previously performed experiments to identify, using a machine learning model, candidate materials that, based on the data, may satisfy the specified manufacturing parameters. After identifying the candidate materials, experiments are performed on these candidate materials to determine whether any are improvements over known materials with respect to the manufacturing parameters. Problematically, these existing systems provide no indicia of how likely a set of candidates is to contain a candidate that satisfies the parameters, and thus using such systems may be an expensive and time-consuming process that ultimately yields no viable materials.